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Assuring Fruit and Vegetable Product Quality and Safety Through the Handling and Marketing Chain


<OL> <LI> Develop, evaluate and apply rapid non destructive sensor technology for quantitative measurement of fruit and vegetable quality. <LI> Develop, evaluate, and apply rapid sensing technologies to assure food safety including bio-security, purity, and integrity of produce.

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NON-TECHNICAL SUMMARY: A. Fruit and vegetable quality needs to be rapidly and non-destructively assessed for increased value. B. Fruit and vegetables need to be rapidly assessed for bacterial contamination to improve consumer confidence in food safety. This project will encourage development of rapid and non-destructive quality assessment and contamination identification in fruits and vegetables. <P>

APPROACH: Spectroscopy Method: Experiments will be conducted using the FTS-6000 spectrometer. Instrumental parameters will be optimized ans spectra of liquid products will be obtained using Attenuated Total Reflection accessory and the solids and surfaces will be evaluated by Photoacoustic spectroscopy. The Spectra will be qualitatively characterized and quantitatively analyzed for food quality estimates by multivariate statistics. Ultrasound Experiments: The instrument will be optimized for measurements and the transducer variations will be accounted for and calibrated. The sample will be analyzed by pulse-echo or transmission method and the spectra analyzed using neural networks. Headspace samples from apples inoculated with surrogate E. Coli strains will be evaluated using a portable electronic nose (Cyranose 320) to determine the feasibility of early and rapid detection of pathogens in pre-juicing operations. Detection thresholds will be established.
PROGRESS: 2002/10 TO 2007/09<BR>
OUTPUTS: The portable Cyranose 320 (Cyrano Sciences) electronic nose was able to correctly distinguish apples without e. coli k12 from those contaminated with e. coli k12 in 88% of the samples. It was also able to distinguish between different concentrations of e. coli k12. A prototype automated inspection system was developed to classify apples based on bruising in real time. Raman spectroscopy was used for apple bruise testing. Fifty Delicious apples were randomly divided into two groups, 25 apples for light bruise group (falling height 50 mm), and 25 for heavy bruise (falling height 300 mm); The PCA and CVA model analysis satisfactorily classified the apples by bruise on-line. FTIR spectroscopy could detect glycerol contamination in wine up to about 0.5%. FTIR spectroscopy was successfully used to differentiate bacteria both at the genus and the strain levels in buffer as well as in apple juice. Two gram-positive pathogenic bacteria (Enterococcus faecium and Bacillus cereus) and three gram-negative pathogenic bacteria (Salmonella enteritidis, Yersinia enterocolitis and Shigella boydii) and five E. coli strains (O103, O55, O121, O30 and O26) could be differentiated with up to 95% accuracy. FTIR was also explored as a tool for counting microorganisms. A sensitivity of up to 1000 CFU/ml was achieved. Tests using the individual \'zNose\' and Cyranose 320 instruments showed that both the e-nose and the zNose could distinguish healthy from damaged apples after about 5-7 days. Dynamic selective fusion achieved an average 1.8% and a best 0% classification error rate in a total of 30 independent runs. The static selective fusion approach resulted in a 6.1% classification error rate, which was not as good as using individual sensors (4.2% for the Enose and 2.6% for the zNose) if only selected features were applied. Simply adding the Enose and zNose features without selection (non-selective fusion) worsened the classification performance with a 32.5% classification error rate. This indicated that the feature selection using the CMAES is an indispensable process in multisensor data fusion, especially if multiple sources of sensors contain much irrelevant or redundant information. Research continued on the application of surface plasmon resonance biosensors to detect pathogenic microorganisms, especially different varieties of pathogenic Escherichia coli. The SPR imaging instrument is currently being tested using an array of antibodies to detect four E. coli serotypes simultaneously (O157, O101, O128, and O148). SPR Imaging is an extension of the traditional SPR devices that are well known for their high sensitivity, real-time monitoring, and label-free detection of probe-target interactions. SPR imaging offers improvements over traditional SPR because it allows for multiple probe/target interactions to be monitored simultaneously. <BR>PARTICIPANTS: Paul Heinemann - principle investigator, oversaw research utilizing electronic nose for pathogen detection and znose/enose fusion for detecting damage in apples, also oversaw surface plasmon resonance sensor development. Joseph Irudayaraj - principle investigator, oversaw research utilizing FTIR spectroscopy and znose for adulterants in honey and wine, also for detecting pathogen contamination on food surfaces and in liquids. The National Honey Board and Electronic Sensor Technologies (manufacturer of the zNose) provided support for this project. Graduate students, visiting scholars, and post-doctoral scholars who worked on this project include C. Li, X. Gao, M. Paradkar, B. Cho, and M. Gupta. <BR>TARGET AUDIENCES: This project served the scientific community, private industry, and society as a whole. The work further advanced the state of pathogen detection and quality evaluation in food, particularly produce, honey, and wine industries. The public is served because improved safety and quality of food will benefit all consumers.
IMPACT: 2002/10 TO 2007/09<BR>
The success of the portable electronic nose assessment of apples will help detect pathogens before apples are turned into juice or other processed products. The electronic nose would be used by industry as a first-step analysis of incoming product as part of a series of steps to eliminate pathogen contamination. Adulteration of honey by cheaper sugars has been one of the most critical problems to the honey industry. Our research proposes to directly address this issue through a rapid spectroscopic process and impact the quality evaluation and assurance process both at the analysis and consumer level. Addition of artificial sugars makes honey an artificial product while the perception is that it is a natural product with organic and amino acids that impart specific nutritive and medicinal property to honey. A data base has been developed that contains the fingerprint of honey varieties in the US. They include the source (or type), region, and season. This is being provided to the National Honey board, which will be responsible for distribution to the industry. The success of the portable electronic nose and zNose assessment of apples will help detect damage in produce. For example, the technology and approach can help retailers detect damage in bagged apples, where the damage may be hidden by other apples. Rapid methods for pathogen detection such as SPR are important to secure the safety and security of the food supply chain. Efforts in this area are important to the public, federal agencies, processors, and farmers.

Heinemann, Paul
Pennsylvania State University
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